{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.14.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr = np.array([i ** 2 for i in range(1,100)])\n",
    "#parr\n",
    "#parr.dtype\n",
    "#parr[:5]\n",
    "nparr[5]=100.100000\n",
    "nparr[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr2 = np.array([1.0,2.0,1])\n",
    "nparr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,5),dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1]], dtype=int16)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(shape=(3,5),dtype='int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[666.6, 666.6, 666.6, 666.6, 666.6],\n",
       "       [666.6, 666.6, 666.6, 666.6, 666.6],\n",
       "       [666.6, 666.6, 666.6, 666.6, 666.6]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full(shape=(3,5),fill_value=666.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  2.22222222,  4.44444444,  6.66666667,  8.88888889,\n",
       "       11.11111111, 13.33333333, 15.55555556, 17.77777778, 20.        ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 2, 9, 6, 9],\n",
       "       [1, 1, 1, 6, 3],\n",
       "       [0, 5, 6, 6, 7]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10,(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 6, 9, 4, 3],\n",
       "       [1, 0, 8, 7, 5],\n",
       "       [2, 5, 5, 4, 8]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.random.randint(0,10,size=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 6, 9, 4, 3],\n",
       "       [1, 0, 8, 7, 5],\n",
       "       [2, 5, 5, 4, 8]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.random.randint(0,10,size=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39016259, 0.26832981, 0.75366384, 0.66673747, 0.87287954],\n",
       "       [0.52109719, 0.75020425, 0.32940234, 0.29130197, 0.00103619],\n",
       "       [0.6361797 , 0.97933558, 0.91236279, 0.39925165, 0.40322917]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.23174866, -0.91360034, -0.27084407,  1.42024914, -0.98226439],\n",
       "       [ 0.80976498,  1.85205227,  1.67819021, -0.98076924,  0.47031082],\n",
       "       [ 0.18226991, -0.84388249,  0.20996833,  0.22958666,  0.26307642]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0,1,(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function normal:\n",
      "\n",
      "normal(...) method of mtrand.RandomState instance\n",
      "    normal(loc=0.0, scale=1.0, size=None)\n",
      "    \n",
      "    Draw random samples from a normal (Gaussian) distribution.\n",
      "    \n",
      "    The probability density function of the normal distribution, first\n",
      "    derived by De Moivre and 200 years later by both Gauss and Laplace\n",
      "    independently [2]_, is often called the bell curve because of\n",
      "    its characteristic shape (see the example below).\n",
      "    \n",
      "    The normal distributions occurs often in nature.  For example, it\n",
      "    describes the commonly occurring distribution of samples influenced\n",
      "    by a large number of tiny, random disturbances, each with its own\n",
      "    unique distribution [2]_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    loc : float or array_like of floats\n",
      "        Mean (\"centre\") of the distribution.\n",
      "    scale : float or array_like of floats\n",
      "        Standard deviation (spread or \"width\") of the distribution.\n",
      "    size : int or tuple of ints, optional\n",
      "        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
      "        ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
      "        a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
      "        Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray or scalar\n",
      "        Drawn samples from the parameterized normal distribution.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scipy.stats.norm : probability density function, distribution or\n",
      "        cumulative density function, etc.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The probability density for the Gaussian distribution is\n",
      "    \n",
      "    .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n",
      "                     e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n",
      "    \n",
      "    where :math:`\\mu` is the mean and :math:`\\sigma` the standard\n",
      "    deviation. The square of the standard deviation, :math:`\\sigma^2`,\n",
      "    is called the variance.\n",
      "    \n",
      "    The function has its peak at the mean, and its \"spread\" increases with\n",
      "    the standard deviation (the function reaches 0.607 times its maximum at\n",
      "    :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n",
      "    `numpy.random.normal` is more likely to return samples lying close to\n",
      "    the mean, rather than those far away.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] Wikipedia, \"Normal distribution\",\n",
      "           http://en.wikipedia.org/wiki/Normal_distribution\n",
      "    .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability,\n",
      "           Random Variables and Random Signal Principles\", 4th ed., 2001,\n",
      "           pp. 51, 51, 125.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Draw samples from the distribution:\n",
      "    \n",
      "    >>> mu, sigma = 0, 0.1 # mean and standard deviation\n",
      "    >>> s = np.random.normal(mu, sigma, 1000)\n",
      "    \n",
      "    Verify the mean and the variance:\n",
      "    \n",
      "    >>> abs(mu - np.mean(s)) < 0.01\n",
      "    True\n",
      "    \n",
      "    >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n",
      "    True\n",
      "    \n",
      "    Display the histogram of the samples, along with\n",
      "    the probability density function:\n",
      "    \n",
      "    >>> import matplotlib.pyplot as plt\n",
      "    >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n",
      "    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n",
      "    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
      "    ...          linewidth=2, color='r')\n",
      "    >>> plt.show()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.random.normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(15).reshape((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.arange(15).reshape((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[2,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:2,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 13, 12, 11, 10],\n",
       "       [ 9,  8,  7,  6,  5],\n",
       "       [ 4,  3,  2,  1,  0]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[::-1,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,0].ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  6, 11])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   # X[:,1]后面的数字可以用于矩阵降纬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "subX = X[:3,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 5,  6,  7],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "subX[0,0]=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,   1,   2,   3,   4],\n",
       "       [  5,   6,   7,   8,   9],\n",
       "       [ 10,  11,  12,  13,  14]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "subX=X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,   1,   2,   3,   4],\n",
       "       [  5,   6,   7,   8,   9],\n",
       "       [ 10,  11,  12,  13,  14]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subX[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "subX[0,0]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subX[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,   1,   2,   3,   4],\n",
       "       [  5,   6,   7,   8,   9],\n",
       "       [ 10,  11,  12,  13,  14]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,   1,   2,   3,   4],\n",
       "       [  5,   6,   7,   8,   9],\n",
       "       [ 10,  11,  12,  13,  14]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.reshape(-1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "A=np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 1, 2, 3],\n",
       "       [4, 5, 6, 4, 5, 6]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([A,A],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=np.array([1,2,3,4,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 6, 7])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-126-86763fa40a2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
     ]
    }
   ],
   "source": [
    "np.concatenate([X,z.reshape(1,-1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-127-68859d2027f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/core/shape_base.py\u001b[0m in \u001b[0;36mvstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \"\"\"\n\u001b[0;32m--> 234\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
     ]
    }
   ],
   "source": [
    "np.vstack([X,z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1,x2,x3=np.split(x,[3,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "A=np.arange(16).reshape([4,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "A1,A2=np.split(A,[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 0,  1],\n",
       "        [ 4,  5],\n",
       "        [ 8,  9],\n",
       "        [12, 13]]), array([[ 2,  3],\n",
       "        [ 6,  7],\n",
       "        [10, 11],\n",
       "        [14, 15]])]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(A,[2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample,label=np.hsplit(A,[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11, 15])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label[:,0] #降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=np.arange(16).reshape(4,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False,  True],\n",
       "       [False, False,  True, False],\n",
       "       [False,  True, False, False],\n",
       "       [ True, False, False,  True]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y % 3 == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 4,  5,  6],\n",
       "       [ 8,  9, 10],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11, 15])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[Y[:,-1] % 3==0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.linspace(0,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "siny=np.sin(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.10083842,  0.20064886,  0.2984138 ,  0.39313661,\n",
       "        0.48385164,  0.56963411,  0.64960951,  0.72296256,  0.78894546,\n",
       "        0.84688556,  0.8961922 ,  0.93636273,  0.96698762,  0.98775469,\n",
       "        0.99845223,  0.99897117,  0.98930624,  0.96955595,  0.93992165,\n",
       "        0.90070545,  0.85230712,  0.79522006,  0.73002623,  0.65739025,\n",
       "        0.57805259,  0.49282204,  0.40256749,  0.30820902,  0.21070855,\n",
       "        0.11106004,  0.01027934, -0.09060615, -0.19056796, -0.28858706,\n",
       "       -0.38366419, -0.47483011, -0.56115544, -0.64176014, -0.7158225 ,\n",
       "       -0.7825875 , -0.84137452, -0.89158426, -0.93270486, -0.96431712,\n",
       "       -0.98609877, -0.99782778, -0.99938456, -0.99075324, -0.97202182,\n",
       "       -0.94338126, -0.90512352, -0.85763861, -0.80141062, -0.73701276,\n",
       "       -0.66510151, -0.58640998, -0.50174037, -0.41195583, -0.31797166,\n",
       "       -0.22074597, -0.12126992, -0.0205576 ,  0.0803643 ,  0.18046693,\n",
       "        0.27872982,  0.37415123,  0.46575841,  0.55261747,  0.63384295,\n",
       "        0.7086068 ,  0.77614685,  0.83577457,  0.8868821 ,  0.92894843,\n",
       "        0.96154471,  0.98433866,  0.99709789,  0.99969234,  0.99209556,\n",
       "        0.97438499,  0.94674118,  0.90944594,  0.86287948,  0.8075165 ,\n",
       "        0.74392141,  0.6727425 ,  0.59470541,  0.51060568,  0.42130064,\n",
       "        0.32770071,  0.23076008,  0.13146699,  0.03083368, -0.07011396,\n",
       "       -0.17034683, -0.26884313, -0.36459873, -0.45663749, -0.54402111])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x109f0cda0>]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af0d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y,siny)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosy=np.cos(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.99490282,  0.97966323,  0.95443659,  0.91948007,\n",
       "        0.87515004,  0.8218984 ,  0.76026803,  0.69088721,  0.61446323,\n",
       "        0.53177518,  0.44366602,  0.35103397,  0.25482335,  0.15601496,\n",
       "        0.0556161 , -0.04534973, -0.14585325, -0.24486989, -0.34139023,\n",
       "       -0.43443032, -0.52304166, -0.60632092, -0.68341913, -0.75355031,\n",
       "       -0.81599952, -0.87013012, -0.91539031, -0.95131866, -0.97754893,\n",
       "       -0.9938137 , -0.99994717, -0.9958868 , -0.981674  , -0.95745366,\n",
       "       -0.92347268, -0.88007748, -0.82771044, -0.76690542, -0.69828229,\n",
       "       -0.6225406 , -0.54045251, -0.45285485, -0.36064061, -0.26474988,\n",
       "       -0.16616018, -0.06587659,  0.03507857,  0.13567613,  0.23489055,\n",
       "        0.33171042,  0.4251487 ,  0.51425287,  0.59811455,  0.67587883,\n",
       "        0.74675295,  0.8100144 ,  0.86501827,  0.91120382,  0.94810022,\n",
       "        0.97533134,  0.99261957,  0.99978867,  0.99676556,  0.98358105,\n",
       "        0.96036956,  0.9273677 ,  0.88491192,  0.83343502,  0.77346177,\n",
       "        0.70560358,  0.63055219,  0.54907273,  0.46199582,  0.37020915,\n",
       "        0.27464844,  0.17628785,  0.07613012, -0.0248037 , -0.12548467,\n",
       "       -0.2248864 , -0.32199555, -0.41582217, -0.50540974, -0.58984498,\n",
       "       -0.66826712, -0.7398767 , -0.8039437 , -0.859815  , -0.90692104,\n",
       "       -0.94478159, -0.97301068, -0.99132055, -0.99952453, -0.99753899,\n",
       "       -0.98538417, -0.96318398, -0.93116473, -0.88965286, -0.83907153])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113c28048>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c28400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y,siny,color='pink')\n",
    "plt.plot(y,cosy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=np.random.normal(0,200,100000)\n",
    "z1=np.random.normal(0,200,100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'haha')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140e93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(z,z1,alpha=0.1)\n",
    "plt.title('haha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
